An On-Line NMR Techniquewith a Programmable Processor"

K. Razazian J. P. Bobis?

S . L. Dieckman A. C. Raptis

Energy Technology Division Argonne National Laboratory

Argonne, IL 60439

?Northern Illinois University DeKalb, IL 60115

The submitted manuscript has been authored by a contractor of the U.S. Government under contract No. W-3 1-109-ENG-38. Accordingly, the U.S. Government retains a nonexclusive, royalty-free license to publish or reproduce the published form of this contribution, or allow others to do so. for U.S. Government purposes.

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Paper submitted to the IEEE Instrumentation/Measurement Technology Conference, Waltham, MA, April 24-26,1995.

This work was supported by the U.S. Department of Energy. *

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An On-Line NMR Technique with a Programmable Processor* K. Razazian, S.L. Dieckman, A.C. Raptis

Argonne National Laboratory Argonne, IL 60439 J P. Bobis Northern Illinois Univ. DeKalb, IL 601 15

Tel: 708-252-7397 FAX: 708-252-3250

ABSTRACT

Nuclear magnetic resonance (NMR) spectroscopy is used to determine molecular

content of materials, mainly in laboratory measurements. The reduced cost of fast

computer processors, together with recent break throughs in digital signal processor

technology, has facilitated the on-line use of NMR by allowing modifications of the

available technology.

This paper discribes a system and an algo'iithm for improving the on-line

operations. It is base on the time-domain NMR signal detected by the controller and

some prior knowledge of chemical signal patterns. The desired signal can be separated

from a composite signal by using an adaptive line enhancer (ALE) filter. This technique

would be useful for upgrading process procedures in on-line manufacturing.

INTRODUCTIO N

NMR spectroscopy is the study of the magnetic properties of nuclei and the

relationships of those properties to nuclear structure of matter. One of the most common

methods to detect the NMR signal is by a pulsed NMR experiment. In this technique, the

NMR signal can be observed by an additional oscillating magnetic field that is

perpendicular to the external static field. When the frequency of the oscillating field is

adjusted to match the spinning nuclei, torque on the precessing angular moment of

magnitization vector causes the vertor angle to change with its static field. This

transition, known as the NMR spectrum or relaxation time of nuclei, results in a net

bsorption of energy by protons, which can be detected electrically. Data received by this

time domain phenomenon can provide much information on the chemical properties and

contents of materials. To implement the operation a portable NMR controller based on

the TMS320C30 digital signal processor (DSP) chip has been developed. The controller

transmits a RF signal to a sample in a large homogeneous magnetic field, and the

resonance absorption of the radio frequency signal is detected in real time. Based on a

some prior knowledge of the desired signal, an adaptive line enhancer filter is used to

suppress the unwanted signals. The ALE filter is a special form of adaptive noise

canceller designed to restrain the interference component of input while passing the

desired signal. It consists of an interconneced delay element and adaptive filter. The

adaptive filter output is subtracted from the input signal to produce the estimation error,

which can then be used to update the tap weights of the filter. Output of the adaptive

filter is used to display the existence of known chemicals and their concentrations.

Theorv

The theory of NMR is complex and requires an extensive understanding of quantum

mechanics. While the main focus of this paper is not to describe NMR theory, a modest

explanation of the basic theory will be useful for understanding the hardware design.

Based on quantization theory, if nuclei are placed in an external magnetic field

(BJ, they will eventually align with the direction of the field and exhibit a circular

motion about the magnetic field (conventionally, the z axis). The frequency at which

nuclei spin, called the Larmor frequency is directly proportional to the magnetic field

o=y*B, (1)

where y is the gyromagnetic ratio defined as the moment divided by the angular

momentum[ex pulse Nmr]. For example, the Larmor frequency of a proton in a magnetic

field of I Tesla is equal to 42.577 MHz.

Now consider applying an external magnetic field to a sample. Initially the nuclei

are in state of randomness (Fig l), but over a period of time the protons become partially

oriented in the direction of the field and attain some degree of alignment

M,.(magnetization vector). This development is known as Longitudinal relaxation time

and forms an exponential curve with a time constant T,.

X

Y Y

Fig.1. (a) Initially in random orientation (b) Time for proton to align with field B,,.(c) Vector representation of (b).

In a pulsed NMR experiment, an additional magnetic field B, is employed to a

sample at its Larmor frequency causes the magnetization vector to rotate. For instance

&er a 90" pulse is applied (Fig. 2), the magnetization vector M, lies along the x axis.

After this transition, M, begins to decay as the system comes back to equilibrium and to

regrow along the z axis direction again. During this process, the magnetization will

induce a decaying sinusoidal voltage (free induction decay, FID) at the rate of exp(-t/T,)

in a pickup coil in the x-y plane.

Fig. 2.(a) Sample in magnetic field (b) M,after 90" pulse (c) Mz regrows along the z-axis (d) Free induction decay

The time constant T2 (known as Transverse relaxation time) of the decaying sinusoidal

signal or FID depends directly on the chemical contents and molecular bounding of the

matter. Measuring the T2 could provide significa& information on molecular structure.

As stated earlier, the ALE technique has been applied to analyze mD spectra.

ALE operation Fig. 3) can be briefly described as follows. A fixed delay A is inserted in

the desired input, which is drawn directly from the primary input. The delay components,

with sufficient length, cause a simple phase shift between desired the signal and the input

signal. Based on the least-mean-square (LMS) algorithm, the adaptive filter uses error

signal to form a transfer function so that the unwanted signals cancel each other at the

summing junction.

LMS

Fig. 3 Adaptive line enhancer(ALE)

The LMS algorithm for the adaptive line enhancer can be shown as follows

w,+,(n) = w, ( n ) + 2 p x(i)x(i -A - n) - x( i - A - n ) x r ( i - A - k)w, (k)] n = 41, .. , k - 1 (2) k=iJ

where w,(n) represents the weights of the filter and k is the number of weights. The

parameter p is a constant that controls the stability and rate of convergence. If eq. 2 is

stable, then wi converges to the solution of the Wiener-Hopf maxtrix equation [ 1.

Therefore,

With the Wiener-Hopf matrix solution being

R * w * = P (4) where R = @(k) = E(x(i)*x(i+k)) is the autocorrelation matrix and P is defined as a

column vector with elements @@+A).

The mean square error can be shown by covariance matrix of the weight vector as [I

equation 6 reveals that the minimum value of 5 depends on the power or energy of the

primary signal on the optimal weight vector as well as on autocomelation of input signal.

Design Cons iderations and Simulation Results

The underlying principles in construction of an NMR spectrometer are briefly

described below. The basis of any NMR system is the pulse controller that is used to

measure the frequency of a nuclear resonance with sufficient accuracy. In principle,

NMR spectroscopy involves generation of two RF pulses. The architecture in Fig. 4

shows this operation.-The system consist of four blocks of tiist-idfirst-out

memory where the wave and phase information is stored.

TCLKZ

~

I TMS320C30 El- Processor

Serial port 1 I 1

I 4 I Am

w-wl Low-Pass

I Phase Sensitive I

FIFO)

1 I 1 PC I I Address L i n e Data Line I

I I

I Bus Transceiver

I

I Detector Down-Conversion Sinlw le) Frequency Doubler

Low-Pass Filter I Anti-Alias Filter

I

I -7

For External Application

Receiver Application

To W R Probe

Fig. 4. A Block diagram of the NMR Controller

Memory blocks 1 and 2 are assigned to produce two pulses with sufficient delay.

1.

Parameters such as period, delay, and width of the pulse can be adjusted to be directly

proportional to the frequency of the timer clock TCLK on the TMS320C30.

TCLK < Transmitting Puls Memory #1

Transmitting RF

Receiving pulse I I Memory #2

Fig 5. Timing diagram of NMR controller.

As shown in Fig. 5, the f t pulse initiates a gated sinusoid by allowing Memory

#3 to flash its data to the An> chip. The end of the pulse is denoted by a status flag.

Frequency of the waveform is chosen to be equivalent to the Larmer frequency (a) of

the nuclei. Figure 5 also shows the timing relationship between the receiving pulse and

the various components within the circuitry. In general, there are three operations that

accrue simultaneously:

1- A gated sinusoid is sent to an external mixer for down conversion of the output signal.

Because the FID is oscillating in high-frequency the DSP can not digitally record the

signal.

2- The external analog switch is enabled to transfer the N M R signal to the mixer.

3- The DSP is triggered by an external interrupt pin. This permits the DSP to start

collecting data and executes the ALE procedure that distinguishes the desired signal from

the composite signal.

To start a new cycle, the C30 processer uses an external flag (XF1) to initialize

FIFO memory. When XFl is set to zero, the stack pointer is directed to the top of the

stack and the clock drivers are enabled.

Frequency domain FID 1 : I I I I

0.8 I . ....................................

0.7 .................... c I

..........................................

0 200 600 800 lo00 1200 1400 Frequency Hz

Frequency domain FID

200 lo00 1200

Fig. 6. Spectrum of composite signal Fig. 7. Spectrum of the desired signal, after passing through ALE Output from ALE

When FID data are recorded by the DSP, the ALE routine is implemented to

identify the desired signal. A computer simulation was carried out to demonstrate the

detection of water in a composite spectra by applying the ALE procedure. Data ware

collected from a series of off-line laboratory experiments. The FID spectrum of the input

signal is shown in Fig 5, and the final result is shown in Fig. 6. It is clear that the system

removed the unwanted background and kept the desired signal at 450 Hz.

Conclusion

In this paper, development of an on-line controller based on the TMS320C30

processor has been described for measuring of nuclear resonance using an adaptive line

enhancer (ALE) method, This approach does not require an external reference signal for

detection and observation of the desired signal. As shown in Fig. 6, an &E filter can

also be used to detect a weak signal in the presence of strong components and

background noise. such systems have a high potential for on-line monitoring applications .. and may contribute greatly to improved process control. The main objective of this work

has been to improve the NMR technique for applicability with reasonable accuracy as an

on-line method rather than as a laboratory standard.

Referencq

[ 11 Chen, C. and D.I. Hoult, “Biomedical Magnetic Resonance Technology”, Adam Hdger, Inc., New York, NY. 1984.

[2] Fukushima, E. and S.B. Roeder, “Experimental Pulse NMR a Nuts and Bolts Approach”, Addison-Wesley, Inc., Massachusetts. 198 1.

[3] Haykin, S. “Adaptive Filter Theory”, Prentice-Hall, Inc., Englewood Cliffs, N.J. 1991.

[4] Shaw, D. “Fourier Transform NMR. Spectroscopy”, Elsevier, Inc., New York, NY. 1984.

[5] Texas Instruments, Inc. “TMS32OC3x User’s Guide”, User’s Manual, 1994.

[6] Windrow, B. and S.D. Stems, “Adaptive Signal Processing”, Prentice-Hall, I~c. , Englewood Cliffs, N.J. 1985.

_I

[7] Widrow, B. “Adaptive Noise Cancelling: Principles and Applications,” Proc. IEEE, V O ~ . 63, pp. 1692-1716, D ~ C . 1975.

[8] Widrow, B. “Adaptive Filter” in Aspects of Network and System Theory, .ed. R. E. Kalman and N. DeClaris, Holt, Rinehart and Winston, New York. 1971.

[9] Zeidler, J. R. et al. “ Adaptive enhancement of Multiple Sinusoids in Uncomlated Noise”, IEEE Trans. Acoust. Speech Signal Process, vol. ASSP-26, pp. 240, June 1978.

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